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This paper discusses model-agnostic searches for new physics at the LargeHadron Collider (LHC) using anomaly-detection techniques for the identificationof event signatures that deviate from the Standard Model (SM). We investigateanomaly detection in the context of machine-learning approaches usingautoencoders, and illustrate expected shapes of invariant masses in the outlierregion using Monte Carlo simulations. Challenges and conceptual limitations ofthis approach are discussed.

Event-based anomaly detection for new physics searches at the LHC using machine learning